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Placing Contextless Data: Organizational Sensemaking of Medical Device Telemetry

Garcia Flores, Alejandro LU and Calek, Johana LU (2026) INFM12 20261
Department of Informatics
Abstract
This study investigates how organizations make sense of machine-generated digital trace data, focusing on the interpretation of LUCAS device telemetry at Jolife. While such data is highly granular, it lacks contextual information, creating challenges in translating technical outputs into meaningful representations of real-world use. Adopting a mixed-methods approach, the study combines qualitative interviews with key stakeholders and quantitative analysis of ap-proximately 70,000 log files. Qualitative findings reveal that sensemaking is fragmented across roles, guided by informal heuristics, and structurally blocked by missing contextual metadata distinguishing clinical from non-clinical use. Quantitative analysis identifies five... (More)
This study investigates how organizations make sense of machine-generated digital trace data, focusing on the interpretation of LUCAS device telemetry at Jolife. While such data is highly granular, it lacks contextual information, creating challenges in translating technical outputs into meaningful representations of real-world use. Adopting a mixed-methods approach, the study combines qualitative interviews with key stakeholders and quantitative analysis of ap-proximately 70,000 log files. Qualitative findings reveal that sensemaking is fragmented across roles, guided by informal heuristics, and structurally blocked by missing contextual metadata distinguishing clinical from non-clinical use. Quantitative analysis identifies five op-erational regimes, i.e., prolonged therapeutic, dynamic interaction, technical fault, mechani-cally stable baseline, and ultra-stable unloaded operation, which resolve into three usage fami-lies through joint inference. Integrating both strands, we extend Koesten et al.'s data sense-making framework with three contributions: placement collapse as a failure mode, the dimen-sion-specific role of computational apparatuses, and joint inference as constitutive of placing in contextless trace data. The study contributes to Information Systems research by theorizing how organizations interpret context-impoverished telemetry and offers practical implications for medical device manufacturers and trace-data analytics. (Less)
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author
Garcia Flores, Alejandro LU and Calek, Johana LU
supervisor
organization
alternative title
A mixed-methods embedded case study of organic data interpretation of a mechanical chest compres-sion device
course
INFM12 20261
year
type
H1 - Master's Degree (One Year)
subject
keywords
Organizational Sensemaking, LUCAS, Digital Trace Data
language
English
id
9239970
date added to LUP
2026-06-16 18:54:41
date last changed
2026-06-16 18:54:41
@misc{9239970,
  abstract     = {{This study investigates how organizations make sense of machine-generated digital trace data, focusing on the interpretation of LUCAS device telemetry at Jolife. While such data is highly granular, it lacks contextual information, creating challenges in translating technical outputs into meaningful representations of real-world use. Adopting a mixed-methods approach, the study combines qualitative interviews with key stakeholders and quantitative analysis of ap-proximately 70,000 log files. Qualitative findings reveal that sensemaking is fragmented across roles, guided by informal heuristics, and structurally blocked by missing contextual metadata distinguishing clinical from non-clinical use. Quantitative analysis identifies five op-erational regimes, i.e., prolonged therapeutic, dynamic interaction, technical fault, mechani-cally stable baseline, and ultra-stable unloaded operation, which resolve into three usage fami-lies through joint inference. Integrating both strands, we extend Koesten et al.'s data sense-making framework with three contributions: placement collapse as a failure mode, the dimen-sion-specific role of computational apparatuses, and joint inference as constitutive of placing in contextless trace data. The study contributes to Information Systems research by theorizing how organizations interpret context-impoverished telemetry and offers practical implications for medical device manufacturers and trace-data analytics.}},
  author       = {{Garcia Flores, Alejandro and Calek, Johana}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Placing Contextless Data: Organizational Sensemaking of Medical Device Telemetry}},
  year         = {{2026}},
}